1. Field of the Invention
The present invention relates to a fault detection and diagnosis, more particular to a fault monitoring method of a continuous annealing process based on recursive kernel principal component analysis.
2. The Prior Arts
With increasing complexity of industrial processes, the requirement for reliability, availability and security is growing significantly. Fault detection and diagnosis (FDD) are becoming a major issue in industry. The actual production process has different characteristics, like linear, nonlinear, time-invariant, time-varying, etc. For the different production processes, we should use different fault monitoring methods so as to effectively monitor the fault. Continuous annealing process is a complex time-varying nonlinear process.
For nonlinear characteristics of the industrial process, some scholars have proposed a kernel principal analysis (KPCA) method. KPCA projects nonlinear data to high-dimensional feature space by nonlinear kernel function, then performs a linear PCA feature extraction in the feature space. KPCA is to perform PCA in high-dimensional feature space, which is not necessary for solving nonlinear optimization problems, and compared with other nonlinear methods it does not need to specify the number of the principal component before modeling, but KPCA method has disadvantage. KPCA is an approach based on the data covariance structure where the principal component model is time-invariant. In the actual industrial process, the mean, variance, correlation structure of process variables under normal conditions will be changed slowly due to sensor drift, equipment aging, raw material change and reduced catalyst activity, etc. Compared with the process fault the changes are slow, which belongs to the normal process operation. When the time-invariant principal component model is applied to time-varying process, it may cause false alarms. Therefore it is necessary to propose a feasible method to solve the time-varying nonlinear problems.